code: def calculate_val(
      thresholds,
      embeddings1,
      embeddings2,
      actual_issame,
      far_target,
      nrof_folds=10,
  ):
      assert embeddings1.shape[0] == embeddings2.shape[0]
      assert embeddings1.shape[1] == embeddings2.shape[1]
      nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
      nrof_thresholds = len(thresholds)
      k_fold = LFold(n_splits=nrof_folds, shuffle=False)
  
      val = np.zeros(nrof_folds)
      far = np.zeros(nrof_folds)
  
      diff = np.subtract(embeddings1, embeddings2)
      dist = np.sum(np.square(diff), 1)
      indices = np.arange(nrof_pairs)
  
      for fold_idx, (train_set, test_set) in enumerate(
          k_fold.split(indices)
      ):
          # Find the threshold that gives FAR = far_target
          far_train = np.zeros(nrof_thresholds)
          for threshold_idx, threshold in enumerate(thresholds):
              _, far_train[threshold_idx] = calculate_val_far(
                  threshold, dist[train_set], actual_issame[train_set]
              )
          if np.max(far_train) >= far_target:
              f = interpolate.interp1d(
                  far_train, thresholds, kind="slinear"
              )
              threshold = f(far_target)
          else:
              threshold = 0.0
  
          val[fold_idx], far[fold_idx] = calculate_val_far(
              threshold, dist[test_set], actual_issame[test_set]
          )
  
      val_mean = np.mean(val)
      far_mean = np.mean(far)
      val_std = np.std(val)
      return val_mean, val_std, far_mean
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import argparse
  import sys
  import numpy
  from scipy import misc
  from sklearn.model_selection import KFold
  from scipy import interpolate
  import sklearn
  import cv2
  import math
  import datetime
  import pickle
  from sklearn.decomposition import PCA
  import mxnet
  from mxnet import ndarray
label_desc: Use image node_id 3 for calling a global function with example usage: calculate_val(thresholds, embeddings1, embeddings2, actual_issame, far_target, nrof_folds) and returns: val_mean,  val_std,  far_mean